An Open Context "types" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Iowa Site Files" data publication.

This research will develop tools and methods to generate, simulate, and evaluate evolvable habitation architectures for long-duration space exploration during early-phase concept development. Future human space exploration requires critical, complex habitation systems to maintain crew performance and wellbeing. As we extend our reach into the solar system, these habitats must provide additional capability within the constraints of the space environment, limited budgets, and launch and lander capacity among others. The augmentation and modification, or evolution, of the habitation functionality over time is made possible through the strategic architecting of evolvable systems. In particular, this research will investigate habitat class, modularity, and reusability as high-level driving parameters of evolvable architectures. Habitat models will be created such that engineers can simulate system behavior not only within a timescale of a single mission of days or months, but over multi-mission campaigns of years or decades. The models will be used to objectively generate and evaluate system architectures within a multi-disciplinary design optimization framework. This will significantly reduce the person-hours of engineers and architects needed for early-phase concept trade studies. The methodologies and associated tools developed are necessary components in a larger fully integrated, dynamic habitat modeling capability for human exploration habitation systems with the ability to address uncertainties regarding campaign element phasing, technology transition, and inter-element dependencies. The products of this research can be applied to, and have broad implications for, sustainable development of new terrestrial and Earth-independent permanent settlement.

An Open Context "types" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Indiana Site Files" data publication.

This is the historic impact factors of Habitation computed for each year in CSV format. The first column shows the exaly JournalID for mixing this table with those of other journals

Egypt Number of Housing Units: Census: Urban: Habitation data was reported at 4,037,139.000 Unit in 2017. This records a decrease from the previous number of 7,870,467.000 Unit for 2006. Egypt Number of Housing Units: Census: Urban: Habitation data is updated yearly, averaging 5,395,117.500 Unit from Dec 1986 to 2017, with 4 observations. The data reached an all-time high of 7,870,467.000 Unit in 2006 and a record low of 4,037,139.000 Unit in 2017. Egypt Number of Housing Units: Census: Urban: Habitation data remains active status in CEIC and is reported by Central Agency for Public Mobilization and Statistics. The data is categorized under Global Database’s Egypt – Table EG.EB012: Number of Housing Units: Census: by Type of Use: Urban.

Egypt Number of Housing Units: Census: Urban: Habitation: Urban Governorates data was reported at 1,129,030.000 Unit in 2017. This records a decrease from the previous number of 3,504,068.000 Unit for 2006. Egypt Number of Housing Units: Census: Urban: Habitation: Urban Governorates data is updated yearly, averaging 2,525,813.000 Unit from Dec 1986 to 2017, with 4 observations. The data reached an all-time high of 3,504,068.000 Unit in 2006 and a record low of 1,129,030.000 Unit in 2017. Egypt Number of Housing Units: Census: Urban: Habitation: Urban Governorates data remains active status in CEIC and is reported by Central Agency for Public Mobilization and Statistics. The data is categorized under Global Database’s Egypt – Table EG.EB012: Number of Housing Units: Census: by Type of Use: Urban.

Through the public-private partnerships enabled by the Next Space Technologies for Exploration Partnerships - 2 (NextSTEP-2) Broad Agency Announcement, NASA has selected six U.S. companies to help advance the Journey to Mars by developing ground prototypes and concepts for a deep space habitat around the Moon. These companies are:

• Bigelow Aerospace of Las Vegas, NV
• Boeing of Pasadena, TX
• Lockheed Martin of Denver, CO
• Orbital ATK of Dulles, VA
• Sierra Nevada Corporation’s Space Systems of Louisville, CO
• NanoRacks of Webster, TX

This round of NextSTEP selections are part of a phased approach that will catalyze commercial investment in low-Earth orbit and lead to an operational deep space habitation capability for missions in the area of space near the moon, which will serve as the proving ground for Mars during the 2020s. These missions will demonstrate human, robotic and spacecraft operations in a true deep space environment that’s still relatively close to Earth and validate technologies for the longer journey to Mars.

The ground prototypes will be used for three primary purposes: supporting integrated systems testing, human factors and operations testing, and to help define overall system functionality. These are important activities as they help define the design standards, common interfaces, and requirements while reducing risks for the final flight systems that will come after this phase.

Number of Housing Units: Census: Urban: Habitation: Lower Egypt: Gharbia data was reported at 155,582.000 Unit in 2017. This records a decrease from the previous number of 312,164.000 Unit for 2006. Number of Housing Units: Census: Urban: Habitation: Lower Egypt: Gharbia data is updated yearly, averaging 233,044.000 Unit from Dec 1986 to 2017, with 4 observations. The data reached an all-time high of 312,164.000 Unit in 2006 and a record low of 155,582.000 Unit in 2017. Number of Housing Units: Census: Urban: Habitation: Lower Egypt: Gharbia data remains active status in CEIC and is reported by Central Agency for Public Mobilization and Statistics. The data is categorized under Global Database’s Egypt – Table EG.EB012: Number of Housing Units: Census: by Type of Use: Urban.

This dataset concerns habitation of houses in the inner city of Leiden in the period 1890-1899 and 1930-1940. Focal point of the research was the relation between macro-economic developments in the Netherlands and demographic behaviour in Leiden. The data was collected during a seminar on Social History in 1976 and 1977, Leiden University.

The proposed project entails test excavation at two prehistoric habitation sites within the interior of the island to acquire data concerning the chronology of their occupation and the kinds of food remains preserved. These data will be used to address a series of research questions concerning subsistence and settlement patterns during the period between 6300 and 5300 years ago. The research results will relate to a larger-scale project at two coastal sites Im planning to undertake with extramural funding, and it would also serve as a basis for a future research project focused on occupation of the islands interior.

The Deep Space Habitat was closed out at the end of Fiscal Year 2013 (September 30, 2013). Results and select content have been incorporated into the new Exploration Augmentation Module (EAM) Project. The Deep Space Habitat project charter is to 1) Define and mature space habitat concepts and architectures; 2) Transition habitat-related products into demonstration prototypes; 3) Mature habitat-related concepts, technologies and systems; and 4) Focus and infuse habitat-related technologies.

The Deep Space Habitat project delivers concepts for light-weight, safe and reliable exploration habitats capable of: o Supporting humans living and working in space and on planetary bodies o Autonomous operation o Systems failure detection, analysis, and self-repair The Deep Space Habitat project investigates habitation concepts for multiple destinations such as: o Cis-lunar space o Interplanetary Space (to include Near Earth Asteroids) This is to drive out opportunities for commonality, early development investment, and early risk mitigation. This project also focuses on maturing exploration habitation subsystems such as: o Structures and mechanisms o Environmental control and life support systems o Active and passive thermal control systems o Power management and distribution o Avionics o Software management system o Communications o Environmental protection & particulate (dust) mitigation o Mission operations/command & control o Crew systems/interfaces: displays & controls, galleys, quarters o Extravehicular activity & robotics o Instrumentation & sensors o Food supplementation The process for the Deep Space Habitat project to deliver on these objectives include iterative loops of: o Concept definition and development o Systems integration o Testing o Building and outfitting habitation prototypes The habitat prototypes function as a(n) o Technology pull o Test bed o Integration capability to advance NASA's understanding of alternative o Mission architectures o Requirements o Operations concepts definition and validation

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Household

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: No - Special populations: No

UNIT DESCRIPTIONS: - Dwellings: The dwelling is a specific and independent place located within the concession, if there are several, or if it exists as part of the concession itself, is the place used as a living space.

Universe

Persons present in Burkina Faso at the time of the census, as well as Burkinabé persons living abroad (enumerated as emigrants).

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Institut National de la Statistique et de la Démographie

SAMPLE DESIGN: Systematic sample of every 10th dwelling with a random start, drawn by MPC.

SAMPLE UNIT: Household

SAMPLE FRACTION: 10%

SAMPLE SIZE (person records): 1,417,824

Mode of data collection

Face-to-face [f2f]

Research instrument

Single enumeration form that requested information on households and individuals.

Response rate

UNDERCOUNT: 5.05%; undercount rate varies by geography, and was highest in Ouagadougou

Gunung Sewu Karst is situated in the faulted block of Southern Java Zone, Indonesia. The area has been uplifted since the Late Pliocene. Three major uplift phases were reported to have been taking place, resulting in the exposure of Miocene carbonate rocks. Prevailing tropical monsoon climate has made it possible for the carbonate formations to evolve through karstification process. Three phases of the uplifting thereafter have resulted in three karst landform evolution. Karst landform evolution in Gunung Sewu Karst inevitably determined pre-historic human habitation. During the first stage when surface river was active, human settlement occupied open space along river courses. When the caves were exposed in the second stage, human settlement moved to the caves and distributed along dry valleys or near doline ponds. Cave habitations ended when major depression dried out provisions of extensive agricultural land. In the modern era, the situation was inverted in which the human habitation was determined by geomorphologic processes. Soil erosion was accelerated due to deforestation and agricultural land intensifications. Native species were replaced by exotic species commodities. Big mammals mentioned above were extinct.

Explore International Space Station Habitation Module, NASA, project, Study sketche.. through unique data from multiples sources: key facts, real-time news, interactive charts, detailed maps & open datasets

Habitat modification through rural and urban expansions negatively impacts most wildlife species. However, anthropogenic food sources in habitations can benefit certain species. The red fox Vulpes vulpes can exploit anthropogenic food, but human subsidies sometimes also sustain populations of its potential competitor, the free-ranging dog Canis familiaris. As human habitations expand, populations of free-ranging dog are increasing in many areas, with unknown effects on wild commensal species such as the red fox. We examined occurrence and diet of red fox along a gradient of village size in a rural mountainous landscape of the Indian Trans-Himalaya. Diet analyses suggest substantial use of anthropogenic food (livestock and garbage) by red fox. Contribution of livestock and garbage to diet of red fox declined and increased, respectively, with increasing village size. Red fox occurrence did not show a clear relationship with village size. Red fox occurrence showed weak positive relationships with density of free-ranging dog and garbage availability, respectively, while density of free-ranging dog showed strong positive relationships with village size and garbage availability, respectively. We highlight the potential conservation concern arising from the strong positive association between density of free-ranging dog and village size.

The HSB Habitation Living Lab is a 29 apartment residential complex built on the university campus. Some 32 students have their permanent residence in the lab, which is also an open sustainability research and innovation platform. The present data set contains one month of disaggredated water consumption data and a total of 20535 rows of data. Water consumption is measured for individual micro-consumption points. 24 of the apartments are organized in four clusters with six studio apartments, where each apartment has it own kitchen box and private WC, and shares a common kitchen area and two bathrooms. Apart from this there are five apartments consisting of 2-4 rooms each. The dwellers are mainly students in their early-mid twenties. No detailed demographics are available. The following fields are used: time: the time of the consumption in 24h format day_of_month: day of the month (1-31) weekday: weekday name (monday-sunday) value: the reported consumption, in cubic meters. room_id: randomly hashed string to identify room units location: type of room where water is consumed location_type: type of apartment/space water_type: hot or cold water point_of_consumption: type of water utility, e.g. shower, wc, sink NB: The resolution and minimum reported volume is one liter (0.001 cbm) and the reporting frequency of the water meters is 1/600 Hz. The last digit of the time has been randomized to prevent potential cross matching.

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI, created using ArcGIS 10.2.2) values for Nevada during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Summer included telemetry locations (n = 14,058) from mid-March to June. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated using R Software (v 3.13) for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

Geographic coverage

National

Kind of data

Census/enumeration data [cen]

Sampling procedure

Other non-random sample

Mode of data collection

Face-to-face [f2f]

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